The field of the invention is medical imaging, and particularly, the use of medical imaging in the diagnosis and treatment of cancer.
Advanced imaging techniques for brain and other neoplasms acquire a variety of physiological data in addition to anatomic data. These include PET scanning, conventional MRI, MRI-spectroscopy, diffusion imaging, SPECT, perfusion imaging, functional MRI, tumor hypoxia mapping, angiogenesis mapping, blood flow mapping, cell death mapping and other methods. In addition, it is anticipated that new and better agents for use in SPECT, PET, and other imaging will be created and/or identified. These techniques will lead to an improvement in the ability to differentiate tumor from normal tissue.
Traditional display of physiologic images is in several ways insufficient. Physiologic images generated from sources such as PET and SPECT are indistinct (tumors have “blurry” borders), and are anatomically ambiguous. Fusion software has facilitated the viewing of neoplasms represented by PET and SPECT within the context of anatomic detail represented by CT. Integrated PET-CT and SPECT-CT devices have improved registration and fusion of anatomic and physiologic images. Traditionally, fused images are viewed by fading between CT and physiologic images, ranging from 0% CT/100% physiologic images, to 100% CT/0% physiologic images.
In present day treatment planning the creation of a three-dimensional treatment volume often involves the manual, slice-by-slice digital outlining of tumor on sequential tomographic images at a computer workstation. Computers are then used to convert cut-by-cut digital outlines into three-dimensional volumes, which become targets for surgical and/or radiation therapy planning. This process is labor-intensive. More importantly, however this process relies on the judgment of the person, usually a physician, digitizing the slice-by-slice images.
There are several limitations associated with the reliance on human judgment in this capacity. First, different physicians have different levels of experience in interpreting scans. Planning based on volumes generated by inexperienced physicians will be less accurate. Secondly, even for experienced physicians, interpretation of imaging findings is in many cases difficult, and in many instances based on “best guess” decision making. Even for experienced physicians, there will always be inter-observer variability. Thus, in research/protocol situations, outcomes data will not be directly transferable from institution to institution.
For imaging modalities such as spectroscopy or PET, particularly for tumors that invade adjacent structures or soft tissues, the line between tumor and adjacent non-tumorous structures is subjective and indistinct. This creates variability from cut to cut, patient to patient, and physician to physician. More importantly, however, it creates uncertainty with regard to the optimal volume needed to maximize local control while minimizing dose (and damage) to adjacent structures.
Traditional systems that display images or incorporate images into treatment processes consider images as physiologically homogenous. This is despite the fact that tumors are known to be physiologically heterogeneous. The limitations described above, related to display of a tumor's outer boundary, also apply to display of a tumor's internal heterogeneity.
Expert systems have been proposed for many medical applications. An expert system generates an inference based on a stored body of knowledge related to the disease and based on inputs in the form of test results and other information about the patient. Inference engines for expert systems come in many forms such as a Bayesian network, fuzzy logic, a decision tree, a neural network, or a self-organized map.
A Bayesian network includes a conditional probability-based network that relies on Bayes theorem to characterize likelihood of different outcomes based on known prior probabilities (i.e. observed prevalence of a disease) and newly acquired information (i.e. sensor signals). Bayesian networks use causal knowledge and explicitly model probabilistic dependence and independence relationships between different events.
Fuzzy logic provides a mechanism for manipulating uncertain information and variables that do not otherwise permit simple categorization as affirmative or negative. Fuzzy logic describes the application of if-then rules to uncertain information and provides probability of outcomes based on preceding events or conditions. Fuzzy logic relies on probabilistic if-then rules. According to principles of fuzzy logic, the probability that a premise will be true is predictable, and the conclusion that follows will also occur with some probability.
A decision tree provides a method for representing multiple temporal and logical inputs and the possible outcomes based on a combination of those inputs. A decision tree does not entail probabilities associated with branches.
A neural network is a black-box information-processing device having a number of non-linear processing modules connected together by elements that have information storage and programming functions. A self-organized map is a particular type sheet-like neural network array configured to execute an adaptive algorithm capable of learning. The neural network is based on the competitive and unsupervised learning process. Other types of expert systems are also contemplated.
While expert systems have been developed for many applications in the field of medicine, none have been developed for oncology. This is due in part to the fact that oncology is dependent on the analysis of medical images to evaluate the location, size, shape and growth of tumors. This critical information is needed for a proper diagnosis, outcome prognosis and treatment planning, and it is difficult to identify measurable parameters in images that can be input to an expert system.